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Discriminative Learning with Scale Decomposition for person detection

Wuhan University Journal of Natural Sciences 2020年第4期25卷 337-342页

作者： WANG Xiao CHEN Jun LIANG Chao HU Ruimin National Engineering Research Center for Multimedia Software Wuhan UniversityWuhan 430072HubeiChina

person detection,which can locate the person regions in the image,continues to be a hot research topic in both computer vision and signal processing communities.However,detecting person at small scale remains a challenging problem due to the lack of discriminative details in the typical image at small scale.In this paper,we propose a decomposition mapping method which contains two subnets:encoder subnet and decoder subnet.Encoder subnet can exploit decomposition transformation for person regions from big scale to small scale.Decoder subnet reverses the process of the encoder subnet.We add deconvolution network to the decoder subnet to make up for the lost information and a discriminative mapping has been restructured to transform the person regions from the small scale to the big scale.Therefore,person-regions and background-regions can then be separated according to their decomposition positions in the new scale space.The proposed approach is evaluated on two challenging person datasets:Caltech dataset and the KITTI dataset.Compared with SAF R-CNN,the miss rate has been optimized by 3.96%on Caltech person dataset and the mean average precision has been optimized by 1.76%on KITTI person dataset.

关键词： discriminative learning scale decomposition person detection

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DTHN: Dual-Transformer Head End-to-End person Search Network

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Computers, Materials & Continua 2023年第10期77卷 245-261页

作者： Cheng Feng Dezhi Han Chongqing Chen School of Information Engineering Shanghai Maritime UniversityShanghai201306China

person search mainly consists of two submissions,namely person detection and person Re-identification(reID).Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network(CNN)(e.g.,ResNet).While these structures may detect high-quality bounding boxes,they seem to degrade the performance of re-ID.To address this issue,this paper proposes a Dual-Transformer Head Network(DTHN)for end-to-end person search,which contains two independent Transformer heads,a box head for detecting the bounding box and extracting efficient bounding box feature,and a re-ID head for capturing high-quality re-ID features for the re-ID task.Specifically,after the image goes through the ResNet backbone network to extract features,the Region Proposal Network(RPN)proposes possible bounding boxes.The box head then extracts more efficient features within these bounding boxes for detection.Following this,the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds.Extensive experiments on two widely used benchmark datasets,CUHK-SYSU and PRW,achieve state-of-the-art performance levels,94.9 mAP and 95.3 top-1 scores on the CUHK-SYSU dataset,and 51.6 mAP and 87.6 top-1 scores on the PRW dataset,which demonstrates the advantages of this paper’s approach.The efficiency comparison also shows our method is highly efficient in both time and space.

关键词： Transformer occluded attention end-to-end person search person detection person re-ID Dual-Transformer Head

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DAAPS: A Deformable-Attention-Based Anchor-Free person Search Model

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Computers, Materials & Continua 2023年第11期77卷 2407-2425页

作者： Xiaoqi Xin Dezhi Han Mingming Cui School of Information Engineering Shanghai Maritime UniversityShanghai201306China

person Search is a task involving pedestrian detection and person re-identification,aiming to retrieve person images matching a given objective attribute from a large-scale image library.The person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively.The current popular person Search models,whether end-to-end or two-step,are based on anchor boxes.However,due to the limitations of the anchor itself,the model inevitably has some disadvantages,such as unbalance of positive and negative samples and redundant calculation,which will affect the performance of models.To address the problem of fine-grained understanding of target pedestrians in complex scenes and small sizes,this paper proposes a Deformable-Attention-based Anchor-free person Search model(DAAPS).Fully Convolutional One-Stage(FCOS),as a classic Anchor-free detector,is chosen as the model’s infrastructure.The DAAPS model is the first to combine the Anchor-free person Search model with Deformable Attention Mechanism,applied to guide the model adaptively adjust the perceptual.The Deformable Attention Mechanism is used to help the model focus on the critical information and effectively improve the poor accuracy caused by the absence of anchor boxes.The experiment proves the adaptability of the Attention mechanism to the Anchor-free model.Besides,with an improved ResNeXt+network frame,the DAAPS model selects the Triplet-based Online Instance Matching(TOIM)Loss function to achieve a more precise end-to-end person Search task.Simulation experiments demonstrate that the proposed model has higher accuracy and better robustness than most person Search models,reaching 95.0%of mean Average Precision(mAP)and 95.6%of Top-1 on the CUHK-SYSU dataset,48.6%of mAP and 84.7%of Top-1 on the person Re-identification in the Wild(PRW)dataset,respectively.

关键词： person Search anchor-free attention mechanism person detection pedestrian re-identification

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